scholarly journals Response Transformation and Profit Decomposition for Revenue Uplift Modeling

2019 ◽  
Author(s):  
Robin M. Gubela ◽  
Stefan Lessmann ◽  
Szymon Jaroszewicz
Keyword(s):  
Uplift Modeling ◽  
Response Transformation

scholarly journals Response transformation and profit decomposition for revenue uplift modeling

2020 ◽  
Vol 283 (2) ◽  
pp. 647-661 ◽  
Author(s):  
Robin M. Gubela ◽  
Stefan Lessmann ◽  
Szymon Jaroszewicz
Keyword(s):  
Uplift Modeling ◽  
Response Transformation

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

Uplift modeling and its implications for B2B customer churn prediction: A segmentation-based modeling approach

2021 ◽  
Vol 99 ◽  
pp. 28-39
Author(s):  
Arno De Caigny ◽  
Kristof Coussement ◽  
Wouter Verbeke ◽  
Khaoula Idbenjra ◽  
Minh Phan
Keyword(s):  
Churn Prediction ◽  
Uplift Modeling ◽  
Customer Churn ◽  
Modeling Approach ◽  
Customer Churn Prediction

scholarly journals A survey and benchmarking study of multitreatment uplift modeling

2020 ◽  
Vol 34 (2) ◽  
pp. 273-308 ◽  
Author(s):  
Diego Olaya ◽  
Kristof Coussement ◽  
Wouter Verbeke
Keyword(s):  
Uplift Modeling

NDSRT: An Efficient Virtual Multi-Sensor Response Transformation for Classification of Gases/Odors

2017 ◽  
Vol 17 (11) ◽  
pp. 3416-3421 ◽  
Author(s):  
Ashutosh Mishra ◽  
N. S. Rajput ◽  
Guangjie Han
Keyword(s):  
Sensor Response ◽  
Response Transformation
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